As is generally known, the medical fraternity maintains records of patient treatments for research and development purposes. Such records usually include details pertaining to tests, reports of those tests, diagnoses, and course of action selected for treatment. Many of such records are images, such as images of affected body parts of the patients indicating a disease or abnormality. Such records of thousands of patients being collected over several decades have led to development of an exhaustive database serving as reference data for doctors or clinicians, for example, while assessing new images for signs of a disease. Therefore, the database often serves as a text book for assistance in treatment and diagnosis of a range of diseases. The doctors may study new images and compare the new images to similar images available in the database.
Similarly, as astronomy has become an immensely data-rich field due to numerous digital sky surveys across a range of wavelengths, techniques to automate the search in huge volumes of data are needed for speeding up research process. However, the astronomical images are commonly noisy with objects of diffuse nature. It has been realized that techniques specific to face this problem are needed. To handle the enormous volume of data, not only of the big number of images but also the size of each image file, it is necessary to summarize the information. For some astronomical objects, assessment of pre-researched records in the database is sufficient for quick and accurate identification. By comparing a new astronomical image to similar images available in the database, it becomes easier for researchers to identify a nature of the new astronomical image.
However as the number of records pertaining to medical data and astronomical data increases in their corresponding databases, handling such a large amount of image data poses a significant challenge. It becomes difficult to implement an architecture that enables archiving of such large number of image data that allows quick retrieval of image data on demand at a low cost. Therefore, there is a need for more scalable and efficient solutions.